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    Learning shape statistics for hierarchical 3d medical image segmentation

    Zhang, W. and Yuan, Y. and Li, Xuelong and Yan, P. (2011) Learning shape statistics for hierarchical 3d medical image segmentation. In: UNSPECIFIED (ed.) IEEE International Conference on Image Processing. Institute of Electrical and Electronics Engineers, pp. 2189-2192. ISBN 9781457713040.

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    Accurate image segmentation is important for many medical imaging applications, whereas it remains challenging due to the complexity in medical images, such as the complex shapes and varied neighbor structures. This paper proposes a new hierarchical 3D image segmentation method based on patient-specific shape prior and surface patch shape statistics (SURPASS) model. In the segmentation process, a coarse-to-fine, two-stage strategy is designed, which contains global segmentation and local segmentation. In the global segmentation stage, patient-specific shape prior is estimated by using manifold learning techniques to achieve the overall segmentation. In the second stage, SURPASS is computed to solve the problem of poor segmentation at certain surface patches. The effectiveness of the proposed 3D image segmentation method has been demonstrated by the experiments on segmenting the prostate from a series of MR images.


    Item Type: Book Section
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Sarah Hall
    Date Deposited: 07 Jun 2013 10:45
    Last Modified: 09 Aug 2023 12:33


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